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   "source": [
    "# Classification"
   ]
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   "source": [
    "## Why classification?\n",
    "\n",
    "There are many times where you have to make a decision. Either A or B. Either Emily or Tom (It's 2021. It's ok to be bisexual). This either ... or ... theme is the heart of classification problems."
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    "## How does classification work?\n",
    "\n",
    "Let's say we are classifying an image. The image is said to be either a cat or a dog. But which one? If we are given the information that there are only black cats and white dogs in the image, we would probably apply a heuristic saying that if the object in the image is bright, then it's likely a dog. If it's dark then it's a cat. In machine words, if the image's average is bright (assuming that the cat/dog will occupy the majority of the image), then it's a dog. Or else it's a cat.\n",
    "\n",
    "What we did above is basically mapping from an image to 2 labels, cat and dog. This is how all models solving classification problem work. If you can find a model that maps the input image (or sound or anything) to your desired label, then it's a good classifier."
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